AIMC Topic: Neural Networks, Computer

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Robust deep learning classification of adamantinomatous craniopharyngioma from limited preoperative radiographic images.

Scientific reports
Deep learning (DL) is a widely applied mathematical modeling technique. Classically, DL models utilize large volumes of training data, which are not available in many healthcare contexts. For patients with brain tumors, non-invasive diagnosis would r...

Learning molecular dynamics with simple language model built upon long short-term memory neural network.

Nature communications
Recurrent neural networks have led to breakthroughs in natural language processing and speech recognition. Here we show that recurrent networks, specifically long short-term memory networks can also capture the temporal evolution of chemical/biophysi...

Neural network-based model predictive tracking control of an uncertain robotic manipulator with input constraints.

ISA transactions
This paper proposes a neural network-based model predictive control (MPC) method for robotic manipulators with model uncertainty and input constraints. In the presented NN-based MPC structure, two groups of radial basis function neural networks (RBFN...

An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study.

Reproductive biomedicine online
RESEARCH QUESTION: The study aimed to develop an artificial intelligence model based on artificial neural networks (ANNs) to predict the likelihood of achieving a live birth using the proteomic profile of spent culture media and blastocyst morphology...

Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.

Medical image analysis
The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-...

Reverse graph self-attention for target-directed atomic importance estimation.

Neural networks : the official journal of the International Neural Network Society
Estimating the importance of each atom in a molecule is one of the most appealing and challenging problems in chemistry, physics, and materials science. The most common way to estimate the atomic importance is to compute the electronic structure usin...

Multimodal medical image fusion via laplacian pyramid and convolutional neural network reconstruction with local gradient energy strategy.

Computers in biology and medicine
BACKGROUND: In recent years, numerous fusion algorithms have been proposed for multimodal medical images. The Laplacian pyramid is one type of multiscale fusion method. Although the pyramid-based fusion algorithm can fuse images well, it has the disa...

Comparing regression and neural network techniques for personalized predictive analytics to promote lung protective ventilation in Intensive Care Units.

Computers in biology and medicine
Mechanical ventilation is a lifesaving tool and provides organ support for patients with respiratory failure. However, injurious ventilation due to inappropriate delivery of high tidal volume can initiate or potentiate lung injury. This could lead to...

Localization and recognition of leukocytes in peripheral blood: A deep learning approach.

Computers in biology and medicine
Automatic recognition and classification of leukocytes helps medical practitioners to diagnose various blood-related diseases by analysing their percentages. Different researchers have come up with different algorithms that use traditional learning f...

Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery.

Scientific reports
Machine learning is a well-known approach for virtual screening. Recently, deep learning, a machine learning algorithm in artificial neural networks, has been applied to the advancement of precision medicine and drug discovery. In this study, we perf...